A. Technical Field
The present invention relates to sensors and, more particularly, to systems, devices, and methods of automatically compensating bias errors in MEMS sensors.
B. Background of the Invention
Low-cost inertial MEMS sensors, such angular rate sensors, play an increasingly important role in the consumer electronics market. A gyroscope sensor is an angular rate sensor that determines angular velocity by measuring angular variation. Gyroscopes are used in many applications, including touchless user interface applications, that track the orientation of the device the sensor is mounted on. Orientation information is typically derived from a time integral of the output signal of the gyroscope.
Unfortunately, some angular rate sensors, such as MEMS gyroscope sensors are known to suffer from numerous non-idealities, including thermal hysteresis and manifestations of other temperature dependencies. The most critical non-ideality, however, is the presence of an unwanted offset signal, also known as zero rate level, at the output of the angular rate sensor. This offset signal is typically a bias signal that exists even at conditions where the sensor is in a steady state condition without being exposed to any external forces besides gravity. The presence of such a bias signal makes it difficult to distinguish between a relatively slow and constant actual motion that the sensor undergoes and a parasitic bias signal. This unwanted signal causes an integration error in that the integral of the sensor output signal diverges over time and causes a cumulative bias or drift error that steadily increases with use. Unless compensated for, such bias errors compromise the sensing accuracy and, ultimately, the usefulness of the sensor as a motion and orientation tracking device.
Existing approaches that seek to eliminate bias errors in MEMS gyroscope sensors typically rely on information provided by one or more auxiliary sensors, e.g., accelerometers and a magnetometers. In some existing approaches, bias compensation is based on algorithms that utilize a sensor fusion process to fuse, for example, a gyroscope output signal with a magnetometer signal and an accelerometer signal employing Kalman or complementary filtering methods. Other approaches relate to a priori measurement and calibration of the sensor bias accounting for certain external conditions. However, these indirect approaches are rather computationally complex and require dedicated processing capabilities. What is needed are tools for system designers to overcome the above-described limitations.